Smart City Gnosys

Smart city article details

Title	An Efficient Attribute-Based Participant Selecting Scheme With Blockchain For Federated Learning In Smart Cities
ID_Doc	7806
Authors	Yin X.; Qiu H.; Wu X.; Zhang X.
Year	2024
Published	Computers, 13, 5
DOI	http://dx.doi.org/10.3390/computers13050118
Abstract	In smart cities, large amounts of multi-source data are generated all the time. A model established via machine learning can mine information from these data and enable many valuable applications. With concerns about data privacy, it is becoming increasingly difficult for the publishers of these applications to obtain users’ data, which hinders the previous paradigm of centralized training through collecting data on a large scale. Federated learning is expected to prevent the leakage of private data by allowing users to train models locally. The existing works generally ignore architectures designed in real scenarios. Thus, there still exist some challenges that have not yet been explored in federated learning applied in smart cities, such as avoiding sharing models with improper parties under privacy requirements and designing satisfactory incentive mechanisms. Therefore, we propose an efficient attribute-based participant selecting scheme to ensure that only someone who meets the requirements of the task publisher can participate in training under the premise of high privacy requirements, so as to improve the efficiency and avoid attacks. We further extend our scheme to encourage clients to take part in federated learning and provide an audit mechanism using a consortium blockchain. Finally, we present an in-depth discussion of the proposed scheme by comparing it to different methods. The results show that our scheme can improve the efficiency of federated learning by enabling reliable participant selection and promote the extensive use of federated learning in smart cities. © 2024 by the authors.
Author Keywords	blockchain; CP-ABE; federated learning; smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
5652	0.899	Wang S.; Chen C.; Han B.; Zhu J.	A Trusted And Decentralized Federated Learning Framework For Iot Devices In Smart City	Proceedings - IEEE Congress on Cybermatics: 2024 IEEE International Conferences on Internet of Things, iThings 2024, IEEE Green Computing and Communications, GreenCom 2024, IEEE Cyber, Physical and Social Computing, CPSCom 2024, IEEE Smart Data, SmartData 2024 (2024)
3195	0.889	Hai T.; Wang D.; Seetharaman T.; Amelesh M.; Sreejith P.M.; Sharma V.; Ibeke E.; Liu H.	A Novel & Innovative Blockchain-Empowered Federated Learning Approach For Secure Data Sharing In Smart City Applications	Lecture Notes in Networks and Systems, 735 LNNS (2023)
58229	0.878	Wehbi O.; Wahab O.A.; Mourad A.; Otrok H.; Alkhzaimi H.; Guizani M.	Towards Mutual Trust-Based Matching For Federated Learning Client Selection	2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (2023)
17023	0.875	Sefati S.S.; Craciunescu R.; Arasteh B.; Halunga S.; Fratu O.; Tal I.	Cybersecurity In A Scalable Smart City Framework Using Blockchain And Federated Learning For Internet Of Things (Iot)	Smart Cities, 7, 5 (2024)
23871	0.875	Wehbi O.; Arisdakessian S.; Guizani M.; Wahab O.A.; Mourad A.; Otrok H.; Khzaimi H.A.; Ouni B.	Enhancing Mutual Trustworthiness In Federated Learning For Data-Rich Smart Cities	IEEE Internet of Things Journal, 12, 3 (2025)
47595	0.872	Muhammad M.H.G.; Ahmad R.; Fatima A.; Mohammed A.S.; Raza M.A.; Khan M.A.	Secure And Transparent Traffic Congestion Control System For Smart City Using A Federated Learning Approach	International Journal of Advanced and Applied Sciences, 11, 7 (2024)
26359	0.866	Gandhi M.; Singh S.K.; Ravikumar R.N.; Vaghela K.	Federated Learning In Secure Smart City Sensing: Challenges And Opportunities	Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025)
8048	0.865	Dang T.K.; Tran-Truong P.T.; Trang N.T.H.	An Enhanced Incentive Mechanism For Crowdsourced Federated Learning Based On Contract Theory And Shapley Value	Communications in Computer and Information Science, 1925 CCIS (2023)
38871	0.859	Haripriya R.; Khare N.; Pandey M.; Biswas S.	Navigating The Fusion Of Federated Learning And Big Data: A Systematic Review For The Ai Landscape	Cluster Computing, 28, 5 (2025)
1776	0.858	Singh S.; Rathore S.; Alfarraj O.; Tolba A.; Yoon B.	A Framework For Privacy-Preservation Of Iot Healthcare Data Using Federated Learning And Blockchain Technology	Future Generation Computer Systems, 129 (2022)